We therefore need a score s g (t) for each candidate transcription factor t ∈ T g to assess how likely it is to be involved in the regression model f g.
The off-road motion model f g ( x t g, η t g ) in (2) is selected to be the following constant velocity model with the state vector x g = (x g y g z g v g ψ T, where x g,y g,z g is the 3D location in a global Cartesian reference system, v g is the translational speed in the x g y g -plane, and ψ is the course.
Here, according to the proposed measurement error model, f g is a multivariate Gaussian distribution with a diagonal variance-covariance matrix.
More general models are possible, for example [ 20] model f g with a random forest [ 29] and score a predictor s g (t) with a variable importance measure specific to this model.
Applying the model f g, we map the new protein gnew onto the pharmacological feature space as (8) where w g j is a weight vector and s g is a sequence similarity score.
For example, if we model f g as a linear function (2) f g (X T g ) = ∑ t ∈ T g β t, g X t, then the score s g (t) should typically assess the probability that β t, g is non-zero [ 23].
The prediction models with both clinical and gene expression variables (Models E, F, G, and H) show little or no improvement over the clinical models.
For 2010.0, three models (B, F, G) have smaller data residuals than other models.
Here we also examine the cases of αf = 2.3 and 4.3 (Models F and G, respectively), considering a wide range obtained in experiments (Takasawa et al., 2011).
Fig. 5. Time evolution of grain size distribution for Models F and G in Panels (a) and (b), respectively, for carbonaceous dust.
